path-token-fix

freeforestml/model.py

@@ -727,14 +727,21 @@ class HepNet:
         # save model architecture and weights (only if already trained)
         if len(self.models) == self.cv.k:
             for fold_i in range(self.cv.k):
-                path_token = path.rsplit(".", 1)
+                path_token = path.rsplit("/", 1)
+                file_token = path_token[-1].rsplit(".", 1)
+                
+                if len(file_token) == 1:
+                    file_token.append(f"fold_{fold_i}")
+                else:
+                    file_token.insert(-1, f"fold_{fold_i}")
+                    
                 if len(path_token) == 1:
-                    path_token.append(f"fold_{fold_i}")
+                    path_token = [".".join(file_token)]
                 else:
-                    path_token.insert(-1, f"fold_{fold_i}")
-
+                    path_token = [path_token[0]] + [".".join(file_token)]
+                
                 # this is the built-in save function from keras
-                self.models[fold_i].save(".".join(path_token))
+                self.models[fold_i].save("/".join(path_token))
 
         with h5py.File(path, "w") as output_file:
             # save default model class
@@ -790,13 +797,20 @@ class HepNet:
         # load trained models (if existing)
         with h5py.File(path, "r") as input_file:
             for fold_i in range(cv.k):
-                path_token = path.rsplit(".", 1)
+                path_token = path.rsplit("/", 1)
+                file_token = path_token[-1].rsplit(".", 1)
+                
+                if len(file_token) == 1:
+                    file_token.append(f"fold_{fold_i}")
+                else:
+                    file_token.insert(-1, f"fold_{fold_i}")
+                    
                 if len(path_token) == 1:
-                    path_token.append(f"fold_{fold_i}")
+                    path_token = [".".join(file_token)]
                 else:
-                    path_token.insert(-1, f"fold_{fold_i}")
+                    path_token = [path_token[0]] + [".".join(file_token)]
 
-                model = tensorflow.keras.models.load_model(".".join(path_token))
+                model = tensorflow.keras.models.load_model("/".join(path_token))
                 instance.models.append(model)
 
         # load normalizer